Exploring the link between comorbidities and Alzheimer's dementia in the Australian Imaging, Biomarker & Lifestyle (AIBL) study.

INTRODUCTION: Mounting evidence suggests that certain comorbidities may influence the clinical evolution of Alzheimer's dementia (AD). METHODS: We conducted logistic regression analyses on the medical history and cognitive health diagnoses of participants in the Australian Imaging, Biomarker & Lifestyle study (n = 2443) to investigate cross-sectional associations between various comorbidities and mild cognitive impairment (MCI)/AD. RESULTS: A mixture of associations were observed. Higher comorbidity of anxiety and other neurological disorders was associated with higher odds of AD, while arthritis, cancer, gastric complaints, high cholesterol, joint replacement, visual defect, kidney and liver disease were associated with lower odds of AD. DISCUSSION: This study underscores the links between specific comorbidities and MCI/AD. Further research is needed to elucidate the longitudinal comorbidity-MCI/AD associations and underlying mechanisms of these associations. Highlights: Comorbidities that significantly increased AD odds included anxiety and other neurological disorders.Arthritis, cancer, gastric complaints, high cholesterol, joint replacement, visual defect, kidney and liver disease were associated with lower odds of AD.Alcohol consumption had the most significant confounding effect in the study.Visual-AD association was modified by age, sex, and APOE ε4 allele status.Anxiety-AD and depression-AD associations were modified by sex.

α-Synuclein seed amplification assay detects Lewy body co-pathology in autosomal dominant Alzheimer's disease late in the disease course and dependent on Lewy pathology burden.

INTRODUCTION: Amyloid beta and tau pathology are the hallmarks of sporadic Alzheimer's disease (AD) and autosomal dominant AD (ADAD). However, Lewy body pathology (LBP) is found in ≈ 50% of AD and ADAD brains. METHODS: Using an α-synuclein seed amplification assay (SAA) in cerebrospinal fluid (CSF) from asymptomatic (n = 26) and symptomatic (n = 27) ADAD mutation carriers, including 12 with known neuropathology, we investigated the timing of occurrence and prevalence of SAA positive reactivity in ADAD in vivo. RESULTS: No asymptomatic participant and only 11% (3/27) of the symptomatic patients tested SAA positive. Neuropathology revealed LBP in 10/12 cases, primarily affecting the amygdala or the olfactory areas. In the latter group, only the individual with diffuse LBP reaching the neocortex showed α-synuclein seeding activity in CSF in vivo. DISCUSSION: Results suggest that in ADAD LBP occurs later than AD pathology and often as amygdala- or olfactory-predominant LBP, for which CSF α-synuclein SAA has low sensitivity. HIGHLIGHTS: Cerebrospinal fluid (CSF) real-time quaking-induced conversion (RT-QuIC) detects misfolded α-synuclein in ≈ 10% of symptomatic autosomal dominant Alzheimer's disease (ADAD) patients. CSF RT-QuIC does not detect α-synuclein seeding activity in asymptomatic mutation carriers. Lewy body pathology (LBP) in ADAD mainly occurs as olfactory only or amygdala-predominant variants. LBP develops late in the disease course in ADAD. CSF α-synuclein RT-QuIC has low sensitivity for focal, low-burden LBP.

Suboptimal self-reported sleep efficiency and duration are associated with faster accumulation of brain amyloid beta in cognitively unimpaired older adults.

INTRODUCTION: This study investigated whether self-reported sleep quality is associated with brain amyloid beta (Aß) accumulation. METHODS: Linear mixed effect model analyses were conducted for 189 cognitively unimpaired (CU) older adults (mean ± standard deviation 74.0 ± 6.2; 53.2% female), with baseline self-reported sleep data, and positron emission tomography-determined brain Aß measured over a minimum of three time points (range 33.3-72.7 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status, and time, adjusting for sex and baseline age. RESULTS: Sleep duration <6 hours, in APOE ε4 carriers, and sleep efficiency <65%, in the whole sample and APOE ε4 non-carriers, is associated with faster accumulation of brain Aß. DISCUSSION: These findings suggest a role for self-reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease (AD) neuropathology in CU individuals. Additionally, poor sleep efficiency represents a potential route via which individuals at lower genetic risk may progress to preclinical AD. Highlights: In cognitively unimpaired older adults self-report sleep is associated with brain amyloid beta (Aß) accumulation.Across sleep characteristics, this relationship differs by apolipoprotein E (APOE) genotype.Sleep duration <6 hours is associated with faster brain Aß accumulation in APOE ε4 carriers.Sleep efficiency < 65% is associated with faster brain Aß accumulation in APOE ε4 non-carriers.Personalized sleep interventions should be studied for potential to slow Aß accumulation.

Impact of PET Reconstruction on Amyloid-ß Quantitation in Cross-Sectional and Longitudinal Analyses.

Amyloid-ß (Aß) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aß PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aß-negative [Aß-], 45 Aß-positive [Aß+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aß- or Aß+ visually. For each reconstruction, Aß CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aß- and Aß+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aß-, 33 Aß+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aß- or Aß+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aß CLs were calculated using CapAIBL. Since linear Aß accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aß accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aß accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aß- and the Aß+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aß- CLs increased for a FWHM of 4.5 mm or more, whereas Aß+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aß- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aß accumulators in the Aß- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aß PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aß- and Aß+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aß accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.

Inter-scanner Aß-PET harmonization using barrel phantom spatial resolution matching.

INTRODUCTION: The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aß-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aß-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aß-PET measurements across different scanners. In this study, we propose an Aß-PET harmonization method of matching spatial resolution, as measured via a barrel phantom, across PET scanners. Our approach was validated using paired subject data, for which patients were imaged on multiple scanners. METHODS: In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. RESULTS: Vision-mCT harmonization: Vision reconstruction with FWHM = 4.10 mm and mCT reconstruction with FWHM = 4.30 mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2 mm and the mCT reconstruction with the FWHM of 9.35 mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1 mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2 mm and were selected as the harmonized for each scanner pair. CONCLUSION: Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aß-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Highlights: Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aß-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aß-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aß-PET quantitation measurements.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Alzheimer's disease biomarker utilization at first referral enhances differential diagnostic precision with simultaneous exclusion of Creutzfeldt-Jakob disease.

Most suspected Creutzfeldt-Jakob disease (CJD) cases are eventually diagnosed with other disorders. We assessed the utility of investigating Alzheimer's disease (AD) biomarkers and neurofilament light (NfL) in patients when CJD is suspected. The study cohort consisted of cerebrospinal fluid (CSF) samples referred for CJD biomarker screening wherein amyloid beta 1-42 (Aß1-42), phosphorylated tau 181 (p-tau181), and total tau (t-tau) could be assessed via Elecsys immunoassays (n = 419) and NfL via enzyme-linked immunosorbent assay (ELISA; n = 161). In the non-CJD sub cohort (n = 371), 59% (219/371) had A+T- (abnormal Aß1-42 only) and 21% (79/371) returned A+T+ (abnormal Aß1-42 and p-tau181). In the 48 CJD subjects, a similar AD biomarker profile distribution was observed. To partially address the prevalence of likely pre-symptomatic AD, NfL was utilized to assess for neuronal damage. NfL was abnormal in 76% (25/33) of A+T- subjects 40 to 69 years of age, 80% (20/25) of whom had normal t-tau. This study reinforces AD as an important differential diagnosis of suspected CJD, highlighting that incorporating AD biomarkers and NfL at initial testing is worthwhile.

Exploring the association between cancer and cognitive impairment in the Australian Imaging Biomarkers and Lifestyle (AIBL) study.

An inverse association between cancer and Alzheimer's disease (AD) has been demonstrated; however, the association between cancer and mild cognitive impairment (MCI), and the association between cancer and cognitive decline are yet to be clarified. The AIBL dataset was used to address these knowledge gaps. The crude and adjusted odds ratios for MCI/AD and cognitive decline were compared between participants with/without cancer (referred to as C+ and C- participants). A 37% reduction in odds for AD was observed in C+ participants compared to C- participants after adjusting for all confounders. The overall risk for MCI and AD in C+ participants was reduced by 27% and 31%, respectively. The odds of cognitive decline from MCI to AD was reduced by 59% in C+ participants after adjusting for all confounders. The risk of cognitive decline from MCI to AD was halved in C+ participants. The estimated mean change in Clinical Dementia Rating-Sum of boxes (CDR-SOB) score per year was 0.23 units/year higher in C- participants than in C+ participants. Overall, an inverse association between cancer and MCI/AD was observed in AIBL, which is in line with previous reports. Importantly, an inverse association between cancer and cognitive decline has also been identified.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

Cerebrospinal fluid neurofilament light predicts longitudinal diagnostic change in patients with psychiatric and neurodegenerative disorders.

OBJECTIVE: People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown. METHODS: We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other. RESULTS: Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone. CONCLUSIONS: CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

How Can We Use Mathematical Modeling of Amyloid-ß in Alzheimer's Disease Research and Clinical Practices?

The accumulation of amyloid-ß (Aß) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aß, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aß levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aß (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aß, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.

Lower levels of soluble ß-amyloid precursor protein, but not ß-amyloid, in the frontal cortex in schizophrenia.

We identified a sub-group (25%) of people with schizophrenia (muscarinic receptor deficit schizophrenia (MRDS)) that are characterised because of markedly lower levels of cortical muscarinic M1 receptors (CHRM1) compared to most people with the disorder (non-MRDS). Notably, bioinformatic analyses of our cortical gene expression data shows a disturbance in the homeostasis of a biochemical pathway that regulates levels of CHRM1. A step in this pathway is the processing of ß-amyloid precursor protein (APP) and therefore we postulated there would be altered levels of APP in the frontal cortex from people with MRDS. Here we measure levels of CHRM1 using [3H]pirenzepine binding, soluble APP (sAPP) using Western blotting and amyloid beta peptides (Aß1-40 and Aß1-42) using ELISA in the frontal cortex (Brodmann's area 6: BA 6; MRDS = 14, non-MRDS = 14, controls = 14). We confirmed the MRDS cohort in this study had the expected low levels of [3H]pirenzepine binding. In addition, we showed that people with schizophrenia, independent of their sub-group status, had lower levels of sAPP compared to controls but did not have altered levels of Aß1-40 or Aß1-42. In conclusion, whilst changes in sAPP are not restricted to MRDS our data could indicate a role of APP, which is important in axonal and synaptic pruning, in the molecular pathology of the syndrome of schizophrenia.

Identifying individuals with non-Alzheimer's disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer's disease.

INTRODUCTION: Biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy body disease (LBD), and cerebral amyloid angiopathy (CAA). METHODS: A multilabel non-ADNC classifier using magnetic resonance imaging (MRI) signatures was developed for TDP-43, LBD, and CAA in an autopsy-confirmed cohort (N = 214). RESULTS: A model using demographic, genetic, clinical, MRI, and ADNC variables (amyloid positive [Aß+] and tau+) in autopsy-confirmed participants showed accuracies of 84% for TDP-43, 81% for LBD, and 81% to 93% for CAA, outperforming reference models without MRI and ADNC biomarkers. In an ADNI cohort (296 cognitively unimpaired, 401 mild cognitive impairment, 188 dementia), Aß and tau explained 33% to 43% of variance in cognitive decline; imputed non-ADNC explained an additional 16% to 26%. Accounting for non-ADNC decreased the required sample size to detect a 30% effect on cognitive decline by up to 28%. DISCUSSION: Our results lead to a better understanding of the factors that influence cognitive decline and may lead to improvements in AD clinical trial design.

Physical activity and brain amyloid beta: A longitudinal analysis of cognitively unimpaired older adults.

INTRODUCTION: The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aß) over 15 years in a cohort of cognitively unimpaired older adults. METHODS: PA and Aß measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross-sectional and longitudinal relationships between PA and brain Aß. Moderation analyses examined apolipoprotein E (APOE) ε4 carriage impact on the PA-Aß relationship. RESULTS: PA was not associated with brain Aß at baseline (ß = -0.001, p = 0.72) or over time (ß = -0.26, p = 0.24). APOE ε4 status did not moderate the PA-Aß relationship over time (ß = 0.12, p = 0.73). Brain Aß levels did not predict PA trajectory (ß = -54.26, p = 0.59). DISCUSSION: Our study did not identify a relationship between habitual PA and brain Aß levels. HIGHLIGHTS: Physical activity levels did not predict brain amyloid beta (Aß) levels over time in cognitively unimpaired older adults (≥60 years of age). Apolipoprotein E (APOE) ε4 carrier status did not moderate the physical activity-brain Aß relationship over time. Physical activity trajectories were not impacted by brain Aß levels.

Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease.

Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.

Clarity on the blazing trail: clearing the way for amyloid-removing therapies for Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a complex pathogenesis. Senile plaques composed of the amyloid-ß (Aß) peptide in the brain are the core hallmarks of AD and a promising target for the development of disease-modifying therapies. However, over the past 20 years, the failures of clinical trials directed at Aß clearance have fueled a debate as to whether Aß is the principal pathogenic factor in AD and a valid therapeutic target. The success of the recent phase 3 trials of lecanemab (Clarity AD) and donanemab (Trailblazer Alz2), and lessons from previous Aß clearance trials provide critical evidence to support the role of Aß in AD pathogenesis and suggest that targeting Aß clearance is heading in the right direction for AD treatment. Here, we analyze key questions relating to the efficacy of Aß targeting therapies, and provide perspectives on early intervention, adequate Aß removal, sufficient treatment period, and combinatory therapeutics, which may be required to achieve the best cognitive benefits in future trials in the real world.

Tackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans.

In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.

Longitudinal trajectories of basal forebrain volume in normal aging and Alzheimer's disease.

Dysfunction of the cholinergic basal forebrain (BF) system and amyloid-ß (Aß) deposition are early pathological features in Alzheimer's disease (AD). However, their association in early AD is not well-established. This study investigated the nature and magnitude of volume loss in the BF, over an extended period, in 516 older adults who completed Aß-PET and serial magnetic resonance imaging scans. Individuals were grouped at baseline according to the presence of cognitive impairment (CU, CI) and Aß status (Aß-, Aß+). Longitudinal volumetric changes in the BF and hippocampus were assessed across groups. The results indicated that high Aß levels correlated with faster volume loss in the BF and hippocampus, and the effect of Aß varied within BF subregions. Compared to CU Aß+ individuals, Aß-related loss among CI Aß+ adults was much greater in the predominantly cholinergic subregion of Ch4p, whereas no difference was observed for the Ch1/Ch2 region. The findings support early and substantial vulnerability of the BF and further reveal distinctive degeneration of BF subregions during early AD.

Exploring the significance of lipids in Alzheimer's disease and the potential of extracellular vesicles.

Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.